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from __future__ import absolute_import, division, print_function
import pandas as pd
import xarray as xr
from . import randn, requires_dask
try:
import dask # noqa
except ImportError:
pass
def make_bench_data(shape, frac_nan, chunks):
vals = randn(shape, frac_nan)
coords = {'time': pd.date_range('2000-01-01', freq='D',
periods=shape[0])}
da = xr.DataArray(vals, dims=('time', 'x', 'y'), coords=coords)
if chunks is not None:
da = da.chunk(chunks)
return da
def time_interpolate_na(shape, chunks, method, limit):
if chunks is not None:
requires_dask()
da = make_bench_data(shape, 0.1, chunks=chunks)
actual = da.interpolate_na(dim='time', method='linear', limit=limit)
if chunks is not None:
actual = actual.compute()
time_interpolate_na.param_names = ['shape', 'chunks', 'method', 'limit']
time_interpolate_na.params = ([(3650, 200, 400), (100, 25, 25)],
[None, {'x': 25, 'y': 25}],
['linear', 'spline', 'quadratic', 'cubic'],
[None, 3])
def time_ffill(shape, chunks, limit):
da = make_bench_data(shape, 0.1, chunks=chunks)
actual = da.ffill(dim='time', limit=limit)
if chunks is not None:
actual = actual.compute()
time_ffill.param_names = ['shape', 'chunks', 'limit']
time_ffill.params = ([(3650, 200, 400), (100, 25, 25)],
[None, {'x': 25, 'y': 25}],
[None, 3])
def time_bfill(shape, chunks, limit):
da = make_bench_data(shape, 0.1, chunks=chunks)
actual = da.bfill(dim='time', limit=limit)
if chunks is not None:
actual = actual.compute()
time_bfill.param_names = ['shape', 'chunks', 'limit']
time_bfill.params = ([(3650, 200, 400), (100, 25, 25)],
[None, {'x': 25, 'y': 25}],
[None, 3])
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